+ j^RIt^RIt^RIHuHt^RIHtHt^RIHtRRRllt RRRllt ]PPR4!R R ]P4t]PPR 4!R R ]4tR#)N)nnTensor)_log_api_usage_onceclV^8dQhR\R\R\R\R\R\R\/#inputp block_sizeinplaceepstrainingreturnrfloatintbool)formats"[/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/ops/drop_block.py __annotate__r sD.. ..),.7;.JO.cg. .c\PP4'g4\PP4'g\ \ 4VR8gVR8d\ RV R24hVP^8wd\ RVP R24hV'dVR8XdV#VP4wrgr\W)V4pV^,^8Xd\ RV R24hW,V ,V^,W, ^,W, ^,,,, p \P!WgW, ^,W, ^,3VPVPR 7p V PV 4\P!W^,.^,^R 7p \P !V R W"3V^,R 7p ^V , p V P#4WKP%4,, p V'd#VP'V 4P'V 4V#W ,V ,pV#) a Implements DropBlock2d from `"DropBlock: A regularization method for convolutional networks" `. Args: input (Tensor[N, C, H, W]): The input tensor or 4-dimensions with the first one being its batch i.e. a batch with ``N`` rows. p (float): Probability of an element to be dropped. block_size (int): Size of the block to drop. inplace (bool): If set to ``True``, will do this operation in-place. Default: ``False``. eps (float): A value added to the denominator for numerical stability. Default: 1e-6. training (bool): apply dropblock if is ``True``. Default: ``True``. Returns: Tensor[N, C, H, W]: The randomly zeroed tensor after dropblock. ?4drop probability has to be between 0 and 1, but got .z#input should be 4 dimensional. Got dimensions.block size should be odd. Got  which is even.dtypedevicevaluestride kernel_sizepadding)r))torchjit is_scripting is_tracingr drop_block2d ValueErrorndimsizeminemptyr!r" bernoulli_Fpad max_pool2dnumelsummul_) r r r r r rNCHWgammanoisenormalize_scales &&&&&& rr.r. s& 99 ! ! # #EII,@,@,B,BL)3w!c'OPQsRSTUU zzQ>uzzl,WXX qCx JA!ZA&JA~9*_UVVUQYJMq~/AanWXFX.YZ [E KKq~11>A3EFekkbgbnbn oE U EE%/*Q.a 8E LLvJ;S]gkl]l mE IEkkmsYY['89O 5/ L / LrclV^8dQhR\R\R\R\R\R\R\/#rr)rs"rrr:sD22 22),27;2JO2cg2 2rc\PP4'g4\PP4'g\ \ 4VR8gVR8d\ RV R24hVP^8wd\ RVP R24hV'dVR8XdV#VP4wrgrp \W(W4pV^,^8Xd\ RV R24hW,V ,V ,V^,W, ^,W, ^,,W, ^,,,, p \P!WgW, ^,W, ^,W, ^,3VPVPR 7p V PV 4\P!W^,.^,^R 7p \P !V R W"V3V^,R 7p ^V , p V P#4WLP%4,, p V'd#VP'V 4P'V 4V#W ,V ,pV#) a Implements DropBlock3d from `"DropBlock: A regularization method for convolutional networks" `. Args: input (Tensor[N, C, D, H, W]): The input tensor or 5-dimensions with the first one being its batch i.e. a batch with ``N`` rows. p (float): Probability of an element to be dropped. block_size (int): Size of the block to drop. inplace (bool): If set to ``True``, will do this operation in-place. Default: ``False``. eps (float): A value added to the denominator for numerical stability. Default: 1e-6. training (bool): apply dropblock if is ``True``. Default: ``True``. Returns: Tensor[N, C, D, H, W]: The randomly zeroed tensor after dropblock. rrrrz#input should be 5 dimensional. Got rrrr r#r%)r)r)r))r*r+r,r-r drop_block3dr/r0r1r2r3r!r"r4r5r6 max_pool3dr8r9r:)r r r r r rr;r<Dr=r>r?r@rAs&&&&&& rrDrD:s& 99 ! ! # #EII,@,@,B,BL)3w!c'OPQsRSTUU zzQ>uzzl,WXX qCx JJLMA!ZA)JA~9*_UVVUQY] A 1>A3E!.[\J\2]abaorsas2tu vE KK q~!1>A#5q~7IJRWR]R]fkfrfr E U EE%/*Q.a 8E LL ijj-Q[eij[j E IEkkmsYY['89O 5/ L / Lrr.cjaa]tRt^rtoRtR V3RlV3RllltV3RlRltV3RlRltRtVt V;t #) DropBlock2dz See :func:`drop_block2d`. c 6<V^8dQhRS[RS[RS[RS[RR/#rr r r r rNrrr)r __classdict__s"rrDropBlock2d.__annotate__ws1%S4e`drcT<\SV`4WnW nW0nW@nR#N)super__init__r r r r selfr r r r __class__s&&&&&rrQDropBlock2d.__init__ws" $ rc&<V^8dQhRS[RS[/#rr rr)rrLs"rrrMccVccrc \WPVPVPVPVP 4#z Args: input (Tensor): Input feature map on which some areas will be randomly dropped. Returns: Tensor: The tensor after DropBlock layer. )r.r r r r rrSr s&&rforwardDropBlock2d.forward.E664??DLL$((TXTaTabbrc <V^8dQhRS[/#)rr)str)rrLs"rrrMs#rcVPP RVP RVP RVP R2pV#)z(p=z , block_size=z , inplace=))rT__name__r r r )rSss& r__repr__DropBlock2d.__repr__sC~~&&'s466(-?PPZ[_[g[gZhhi jr)r r r r Fư>) rd __module__ __qualname____firstlineno____doc__rQr]rf__static_attributes____classdictcell__ __classcell__rTrLs@@rrHrHrs/ccrrHrDcXaa]tRt^toRtRV3RlV3RllltV3RlRltRtVtV;t #) DropBlock3dz See :func:`drop_block3d`. c 6<V^8dQhRS[RS[RS[RS[RR/#rJrK)rrLs"rrDropBlock3d.__annotate__s166%6S646e6`d6rc(<\SV`WW44R#rO)rPrQrRs&&&&&rrQDropBlock3d.__init__s 5rc&<V^8dQhRS[RS[/#rWrX)rrLs"rrrurYrc \WPVPVPVPVP 4#r[)rDr r r r rr\s&&rr]DropBlock3d.forwardr_rrh) rdrjrkrlrmrQr]rnrorprqs@@rrsrss&66cccrrs)FriT)r*torch.fxtorch.nn.functionalr functionalr5rutilsrr.rDfxwrapModulerHrsr{rrrse '.b2j n"))8 nc+cr